Integrating the prescriptive and linear approach of NSD with the non-linear approach of service design can enhance value cocreation orientation in service innovation. Then, the objectives of this study are to explore these complementarities, propose an integrated model that enhances value cocreation in service innovation, and evaluate this model. The methodology involved a systematic literature review, focus group, and brainstorming session to propose the model, followed by evaluation through three case studies and expert interviews. As results, the model’s main feature is the development of each service prerequisite through the service design cycle, prescriptively incorporating customer input throughout the service innovation process. Expert interviews and model application indicated that the model achieved its objectives, as the development of each service element was based on a deep...

This study assesses the value capture of a result-oriented Product-Service System offer that constitutes a postharvest solution. Applying the reinforcement learning reward system and general linear models, we identified the Brazilian farmer’s propensities to choose different products and services from the proposed system. Reinforcement learning enables one to understand the choice process by rewarding the attributes selected and applying penalties to those not chosen. Regarding product options, farmers’ most valued attributes were extended capacity, fixed installation, automatic dryer, and CO2 emission control, considering the investigated system. Regarding service options, the farmers opted for maintenance plans, performance reports, no photovoltaic energy, and purchase over the rental modality. These results assist managers through a reward learning system that constantly updates t...

This study assesses the value capture of a result-oriented Product-Service System offer that constitutes a postharvest solution. Applying the reinforcement learning reward system and general linear models, we identified the Brazilian farmer’s propensities to choose different products and services from the proposed system. Reinforcement learning enables one to understand the choice process by rewarding the attributes selected and applying penalties to those not chosen. Regarding product options, farmers’ most valued attributes were extended capacity, fixed installation, automatic dryer, and CO2 emission control, considering the investigated system. Regarding service options, the farmers opted for maintenance plans, performance reports, no photovoltaic energy, and purchase over the rental modality. These results assist managers through a reward learning system that constantly updates t...